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This book constitutes the refereed conference proceedings of the 6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023, held in Lübeck, Germany, during November 9-10, 2023.The 17 full papers and 2 short papers presented in this book were carefully reviewed and selected from 76 submissions. The main conference AMLDA 2023 is renowned for presenting cutting-edge research on all aspects of machine learning as well as important application areas such as healthcare and medical imaging informatics, biometrics, forensics, precision agriculture, risk management, robotics, and satellite imaging.

With the ever-increasing threat of cyber-attacks, especially as the COVID-19 pandemic helped to ramp up the use of digital communications technology, there is a continued need to find new ways to maintain and improve cybersecurity. This new volume investigates the advances in artificial intelligence and soft computing techniques in cybersecurity. It specifically looks at cybersecurity during the COVID-19 pandemic, the use of cybersecurity for cloud intelligent systems, applications of cybersecurity techniques for web applications, and cybersecurity for cyber-physical systems. A diverse array of technologies and techniques are explored for cybersecurity applications, such as the Internet of Things, edge computing, cloud computing, artificial intelligence, soft computing, machine learning, cross-site scripting in web-based services, neural gas (GNG) clustering technique, and more.

This book reports research advances in the area of deep learning, IoT and urban computing, and describes new insights based on deep learning and IoT for urban computing.

Millions of people in developing countries lack adequate access to food. Increased food production and the commercialization of agriculture are cornerstones for increasing food security. Considerable literature focuses on commercialization effects of crop production, but little attention is given to livestock production, integral to mixed farming. By contrast, this study examines the food security effects of smallholder intensified dairying in Ethiopia. Implications for policy formulation are outlined. The research is based on primary data collected from households with and without crossbred cows. Analysis of the food security outcomes of intensified dairying is based on an agricultural household model. Instrumental variable techniques were applied to the panel data to estimate the parameters of the food consumption, calorie intake, and marketed surplus equations. The empirical analysis indicates positive and significant effects of intensified dairying on food security. Such information should be useful to professionals in agricultural development, including agricultural commercialization, food security and dairy development, in developing countries.